U.S. Pat. No. 5,884,230 to Srinivasan et al. titled ‘Method and system for protein modeling’ discloses a method in a computer system for modeling a three-dimensional structure of a model protein in which the modeling is based upon a three-dimensional structure of a template protein and an amino acid sequence alignment of the model protein and the template protein. For each amino acid in the model protein, when the template protein has an amino acid aligned with the amino acid of the model protein, the position of the backbone atom of the amino acid aligned with the amino acid of the model protein is established based on the position of a topologically equivalent backbone atom in the aligned amino acid of the template protein. However, this method works only when the structure of a model protein is already known. Therefore it is not applicable to smaller molecules such as oligopeptides, or to molecules that are chemically different, such as nucleic acids, saccharides, antibiotics, and other organic molecules. Even in the case of protein structure, the method is not applicable to ab initio protein structure determination, in other words, the method cannot be applied if there is no model structure from which to start.
U.S. Pat. No. 6,188,956 to Mayo, et al. titled ‘Apparatus and method for automated protein design’ relates to an apparatus and a methods for quantitative protein design and optimization. The method receives a known protein backbone structure and builds the conformations of the side chains using the Dead-End Elimination method. As above, this method also would work only when the structure of a model protein is already known. Therefore it is not applicable to smaller molecules such as oligopeptides, or to molecules that are chemically different, such as nucleic acids, saccharides, antibiotics, and other organic molecules. Even in the case of protein structure, the method not applicable to ab initio protein structure determination, in other words, if there is no model structure from which to start, the method cannot be applied since it requires an already known protein backbone conformation.
‘Combinatorial Mathematics’, H. J. Ryster, a textbook of combinatorial mathematics gives a definition of Mutually Orthogonal Latin Squares together with an equation to construct them. However, there is no explanation of how the equation is to be incorporated into a computer program. Also there is no mention of any application of Mutually Orthogonal Latin Squares (MOLS) to find the best value of any function, including the potential energy function. Also there is no mention of any application to the determination of the three-dimensional structure of a molecule. Also it only describes how to distribute a set of symbols so as to form a set of MOLS, but it does not describe any method to replace the symbols with the values of the parameters that define any function. Also it does not describe how the calculations are to be analyzed after the construction of the MOLS.
‘Introduction to Combinatorial Mathematics’, by C. L. Liu is another textbook reference on combinatorial mathematics which gives a definition of Mutually Orthogonal Latin Squares together with an equation to construct them. Again however, there is no explanation of how the equation is to be incorporated into a computer program. Also there is no mention of any application of Mutually Orthogonal Latin Squares (MOLS) to find the best value of any function, including the potential energy function. Also there is no mention of any application to determination of the three-dimensional structure of a molecule. Also it only describes how to distribute a set of symbols so as to form a set of MOLS, but it does not describe any method to replace the symbols with the values of the parameters that define any function. Also it does not describe how the calculations are to be analyzed after the construction of the MOLS.
‘Experimental Design and Its Statistical Basis’, D. J. Finney is a textbook on experimental design which gives a method to use Mutually Orthogonal Latin Squares in agricultural and pharmaceutical experiments. However, there is no teaching herein of how MOLS are to be constructed, nor does it specify any computer program to do so, nor does it mention any application to determine molecular structure or to find the best value of a function. Also the analyses of the calculations made in each sub square as stated in this book are more complicated than the one used in the present invention.
‘Genetic Algorithms and Protein Folding’ by S. Schulze-Kremer, Methods in Molecular Biology (D Webster, ed.) 2000 describes the application of genetic algorithms in protein structure prediction. However, the method described fails for ab initio structure prediction. That is, if there is no previous information about the secondary structure, the method does not work. Also the method does not use MOLS in any form in the calculations, and hence is different from the present invention. ‘Global Search for Optimal Biomolecular Structures using Mutually Orthogonal Latin Squares’ by N. Gautham and Z. A. Rafi, Current Science, 1992 is a very preliminary report of the possible use of MOLS in structure prediction. The method was however, crude and not properly formulated. It did not incorporate any equation to predict MOLS, and it did not specify how the symbols of the MOLS are to be related to the values of the variables. It did not specify in detail the method used for analyzing the values calculated in the sub squares. It was not applied to pentapeptides.
‘Revised algorithms for build-up procedure for predicting protein conformations by energy minimization’ by K. D. Gibson and H. A. Scheraga, Journal of Computational Chemistry, 1987 discloses ‘Build-Up Procedure’ to predict protein conformations. This method carries out a truncated search, relaying on the dominance of short-range interactions. Thus, it finds local minima for short fragments by an exhaustive energy-minimization procedure. The number of conformations for the fragments that must be energy minimized and stored at each step increases exponentially. The method becomes unmanageable for polypeptide chains of about 20 amino acid residues. Also there is no mention at all of the use of MOLS in any form in the procedure. ‘On the multiple-minima problem in the conformational analysis of polypeptides’ I. Backbone degrees of freedom for a perturbed a-helix’ by L. Piela and H. A. Scheraga, Biopolymers, 1987 discloses the use of a ‘Self Consistent Electrostatic Field’, based on the idea that a peptide group dipoles in a native conformation must have an approximately optimal orientations in the electric field generated by the whole molecule and its surrounding solvent. The method does not use all the other energy terms, It is therefore specific for only the an electrostatic field. It cannot be applied to small molecules such as peptides, etc. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘Monte Carlo-Minimization approach to the multiple-minima problem in protein folding’ by Z. Li and H. A. Scheraga, Proceedings of the National Academy of Sciences, 1987 discloses a stochastic approach for global optimization of polypeptides and proteins that combines the strength of the Metropolis Monte Carlo method in global combinatorial optimization with that of conventional energy minimization to find local minima. This is not a deterministic search procedure and therefore will not ensure the best structure every time. Also it is very computationally intensive and therefore very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure and hence this method is different from the present invention. ‘New developments of the electrostatically driven Monte Carlo method—Test on the membrane bound portion of melittin’ by D. R. Ripoll, A. Liwo and H. A. Scheraga, Biopolymers, 1998 discloses an iterative procedure for searching the conformational hypersurface of polypeptides. This is not a deterministic search procedure and therefore will not ensure the best structure every time. Also it is very computationally intensive and therefore very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘The multiple-minima problem in the conformational analysis of molecules—Deformation of the potential energy hypersurface by the diffusion equation method’, L. Piela, J. Kostrowicki and H. A. Scheraga, Journal of Physical Chemistry, 1989 discloses the use of the basic idea of the ‘Diffusion Equation Method’ to deform the multivariable function that represents the potential energy in such a manner as to make the shallow wells disappear gradually, while other potential wells grow at their expense. The method is not easily applied in practice to complicated structures. This is not a deterministic search procedure and therefore will not ensure the best structure every time. Also it is very computationally intensive and therefore very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘Molecular dynamics on deformed potential energy hypersurfaces’ by J. Pillardy and L. Piela, Journal of Physical Chemistry, 1995. The ‘Distance Scaling Method’ is a procedure to deform the potential energy hypersurface. The procedure is an extension of the ‘diffusion equation method’ and suffers from the same drawbacks. This is not a deterministic search procedure and therefore will not ensure the best structure every time. Also it is very computationally intensive and therefore very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing’ by J. Lee and H. A. Scheraga, Biopolymers, 1998 combines essential aspects of the build-up procedure and a genetic algorithm. It searches the whole conformational space in its early stages and then narrows the search to smaller regions of low energy. However, searching the entire conformational space requires large amounts of computer time. Therefore it is very computationally intensive and very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘Protein structure can be predicted by global optimization of a potential energy function’ by A. Liwo, J. Lee, D. R. Ripoll, J. Pillardy and H. A. Scheraga, Proceedings of the National Academy of Sciences, 1999 uses a Hierarchical Approach based on two recent developments, a united-residue force field (UNRES) and the CSA method. An extensive conformational search is carried out with CSA using a UNRES force field. This suffers from the same drawbacks as the ‘Conformational space Annealing method’. In other words, it is very computationally intensive and therefore very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘Taboo Search: An approach to the Multiple Minima Problem’, D. Cvijiovic and J. Klinowski, Science, 1995 describes a method, Taboo Search (TS), based on the Glover's taboo search for discrete functions, of solving the multiple minima problem for continuous functions. The method does not indicate any application to molecular structure. Also it is very computationally intensive and therefore very expensive in terms of computer time. Also there is no mention at all of the use of MOLS in any form in the procedure.
‘TRUST: A deterministic algorithm for Global Optimization’ by J. Barthen, V. Protopopescu and D. Reister, Science, 1997 suggests an approach the use of Terminal Repeller Unconstrained Sub-energy Tunneling algorithm (TRUST) to solve continuous global optimization problems. The method, however, does not indicate any application to molecular structure and is also very computationally intensive. Therefore the method is expensive in terms of computer time. Also there is no mention of the use of MOLS in the procedure.